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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
orid: string
arxiv_id: string
title: string
area: string
authors: list<item: string>
arxiv_url: string
openreview_url: string
text_source: string
full_text: bool
vs
n: int64
sigma_sq: double
tau: double
trials: int64
n_partitions_enumerated: int64
exhaustive: bool
rows: list<item: struct<d: int64, frac_fixed: double, sd: double, one_minus_frac: double, eq1_bound: double, lloyd_iters: double, lloyd_err: double, random_init_err: double>>
frac_fixed_at_d16384: double
bound_becomes_informative_at_d: int64
lloyd_err_vs_random_err_highd: list<item: double>
runtime_s: double
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
self.write_rows_on_file()
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 662, in write_rows_on_file
table = pa.concat_tables(self.current_rows)
File "pyarrow/table.pxi", line 6320, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
orid: string
arxiv_id: string
title: string
area: string
authors: list<item: string>
arxiv_url: string
openreview_url: string
text_source: string
full_text: bool
vs
n: int64
sigma_sq: double
tau: double
trials: int64
n_partitions_enumerated: int64
exhaustive: bool
rows: list<item: struct<d: int64, frac_fixed: double, sd: double, one_minus_frac: double, eq1_bound: double, lloyd_iters: double, lloyd_err: double, random_init_err: double>>
frac_fixed_at_d16384: double
bound_becomes_informative_at_d: int64
lloyd_err_vs_random_err_highd: list<item: double>
runtime_s: double
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
text string | status string |
|---|---|
In the high-noise, high-dimensional regime where σ² > n, Lloyd's k-means algorithm fails catastrophically: essentially every partition of the data becomes a fixed point of the algorithm (Theorem 1.1). | unverified |
In the same high-noise regime, Hartigan's k-means algorithm avoids this pathology, having no incorrect fixed points with high probability (Theorem 1.1). | unverified |
Corollary 3.8 bounds the probability that any q-approximately balanced partition is not a fixed point of Lloyd's algorithm by 2^n · n · ρ_q^(d/4) (Corollary 3.8). | unverified |
Corollary 3.12 bounds the probability that any incorrect partition is a fixed point of Hartigan's algorithm by 2^n · ρ_h^(d/4), a substantially tighter bound than Lloyd's case (Corollary 3.12). | unverified |
Theorem 3.4 shows Lloyd's single-sample reassignment step persists misclassification errors once the noise level exceeds σ > (2c̄τ(c−1))/√(c(c+c̄)) (Theorem 3.4). | unverified |
Numerical experiments empirically demonstrate the divergence between Lloyd's catastrophic-failure behavior and Hartigan's robustness as dimension grows, corroborating the theoretical bounds (Section 4). | unverified |
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